Tumor Classification in Well-Differentiated Thyroid Carcinoma and Sentinel Lymph Node Biopsy Outcomes: A Direct Correlation
Bibliographic record
Abstract
BACKGROUND: Predicting locoregional metastasis in well-differentiated thyroid carcinoma (WDTC) is a challenge for thyroid cancer surgeons. Sentinel lymph node biopsy (SLNB) has been shown to be an effective predictive tool. To our knowledge, primary tumor (T) classification has yet to be studied with regard to SLNB. We hypothesized that larger primary tumors would correlate with the rate of malignancy in SLNBs. METHODS: A retrospective chart review was conducted on patients operated for WDTC at the McGill Thyroid Cancer Center over a 36-month period. Patients who underwent a total thyroidectomy and SLNB for WDTC were included in this study. RESULTS: A total of 311 patients were included and separated into two groups (236 negative and 75 positive SLNBs). Among patients with negative SLNBs, 65% had T1 primary tumors, 17% T2, 16% T3, and 2% T4, whereas 18% of patients with positive SLNBs had T1 primary tumors, 5% T2, 45% T3, and 32% T4 (p<0.001). Patients under the age of 45 years had a higher rate of positive SLNs (36% in those <45 years vs. 17% in those ≥ 45 years; p<0.001). CONCLUSIONS: Age (<45 years) and higher T category were found to be associated with a higher rate of positive SLNBs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".